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Inventory tracking plays an important role in modern warehouse management. An automated storage and retrieval system using robots can mitigate manual labor strain, reduce operational and labor costs, and improve efficiency. Path planning is an integral component in the design and application of autonomous vehicles. In this thesis, various path planning algorithms based on RRT (rapidly exploring random trees) and RRT* (optimal rapidly exploring random trees) are studied and compared in three simulated environments with obstacles. Computer simulation results show that, the modified RRT* algorithm with node reuse achieves the best performance (i.e. the shortest path) with reasonable computational cost (run time). The run time of the modified RRT algorithm with node reuse was up to 76% better than the independent path planning RRT algorithm and was up to 88% better for the complementary RRT* algorithms. The total nodes generated was up to 68% for both the modified RRT and RRT* algorithms with node reuse when compared to the independent path planning versions. |
